Normalization of Adverse Drug Events (ADEs), or linking adverse event mentions to standardized dictionary terms, is crucial for harmonizing diverse clinical and patient-reported descriptions, enabling reliable aggregation, accurate signal detection, and effective pharmacovigilance across heterogeneous data sources. The ALTA 2025 shared task focuses on mapping extracted ADEs from documents to a standardized list of MedDRA phrases. This paper presents a system that combines rulebased methods, zero-shot and fine-tuned large language models (LLMs), along with promptbased approaches using the latest commercial LLMs to address this task. Our final system achieves an Accuracy@1 score of 0.3494, ranking second on the shared task leaderboard.